Assessing and combining repeated prognosis of physicians and temporal models in the intensive care

Authors:
Lilian Minne;Tudor Toma;Evert De Jonge;Ameen Abu-Hanna
Affiliations:
Department of Medical Informatics, Academic Medical Center, PO Box 22660, 1100 DD Amsterdam, The Netherlands;Department of Medical Informatics, Academic Medical Center, PO Box 22660, 1100 DD Amsterdam, The Netherlands;Department of Intensive Care, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands;Department of Medical Informatics, Academic Medical Center, PO Box 22660, 1100 DD Amsterdam, The Netherlands
Venue:
Artificial Intelligence in Medicine
Year:
2013

Citing 3
Cited 1

Discovery and inclusion of SOFA score episodes in mortality prediction

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Discovery and integration of univariate patterns from daily individual organ-failure scores for intensive care mortality prediction

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Learning predictive models that use pattern discovery-A bootstrap evaluative approach applied in organ functioning sequences

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Guest Editorial: Artificial intelligence in medicine AIME 2011

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Abstract

Objective: Recently, we devised a method to develop prognostic models incorporating patterns of sequential organ failure to predict the eventual hospital mortality at each day of intensive care unit (ICU) stay. In this study, we investigate using a real world setting how these models perform compared to physicians, who are exposed to additional information than the models. Methods: We developed prognostic models for days 2-7 of ICU stay by data-driven discovery of patterns of sequential qualitative organ failure (SOFA) scores and embedding the patterns as binary variables in three types of logistic regression models. Type A models include the severity of illness score at admission (SAPS-II) and the SOFA patterns. Type B models add to these covariates the mean, max and delta (increments) of SOFA scores. Type C models include, in addition, the mean, max and delta in expert opinion (i.e. the physicians' prediction of mortality). Results: Physicians had a statistically significantly better discriminative ability compared to the models without subjective information (AUC range over days: 0.78-0.79 vs. 0.71-0.74) and comparable accuracy (Brier score range: 0.15-0.18 vs. 0.16-0.18). However when we combined both sources of predictions, in Type C models, we arrived at a significantly superior discrimination as well as accuracy than the objective and subjective models alone (AUC range: 0.80-0.83; Brier score range: 0.13-0.16). Conclusion: The models and the physicians draw on complementary information that can be best harnessed by combining both prediction sources. Extensive external validation and impact studies are imperative to further investigate the ability of the combined model.